Side-by-side comparison
Ling-2.6-1T vs MiMo-V2.5-Pro
| Ling-2.6-1T | MiMo-V2.5-Pro | |
|---|---|---|
| Provider | inclusionAI | Xiaomi |
| Context window Maximum tokens (input + output) the model can process in a single request. Glossary → | 262,144 | 1,048,576 |
| Capabilities Optional capabilities the model advertises: vision (images), tools (function calling), json_mode (structured output). | tools, json_mode | tools, json_mode |
| Input $ / 1M tokens Cost for tokens you send (prompt + context). Cheaper side highlighted. Glossary → | 0.3000 | 1.0000 |
| Output $ / 1M tokens Cost for tokens the model generates. Output is normally 3–5× pricier than input. Glossary → | 2.5000 | 3.0000 |
Frequently asked questions
Which is cheaper, Ling-2.6-1T or MiMo-V2.5-Pro?
Ling-2.6-1T is cheaper than MiMo-V2.5-Pro on a 50/50 input/output blend by about $0.6 per 1M tokens. Exact savings depend on your input-vs-output ratio — use the cost calculator on this page for a workload-specific estimate.
Which has a larger context window, Ling-2.6-1T or MiMo-V2.5-Pro?
MiMo-V2.5-Pro has the larger context window at 1M tokens versus 262k tokens for Ling-2.6-1T. That means MiMo-V2.5-Pro can ingest about 4.0x as much text per request.
What is the difference between Ling-2.6-1T and MiMo-V2.5-Pro?
Ling-2.6-1T comes from inclusionAI; MiMo-V2.5-Pro comes from Xiaomi. They differ in pricing, context window, and supported capabilities — see the side-by-side table on this page for the exact figures, refreshed nightly.